Data-driven Patient-Specific Agent Based Models of Metastatic Melanoma for Immunotherapy Response Prediction

用于免疫治疗反应预测的数据驱动的基于患者特异性药物的转移性黑色素瘤模型

• 批准号: 10831325
• 负责人: James R. Heath
• 金额: $ 14.72万
• 依托单位: INSTITUTE FOR SYSTEMS BIOLOGY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-22 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10831325
• 关键词: Acceleration; Address; Adoptive Cell Transfers; Algorithms; Automobile Driving; Biological; Biopsy; Calibration; Cell physiology; Cells; Clinical; Clinical Data; Clinical Trials; Clinical Trials Design; Combination immunotherapy; Combined Modality Therapy; Communities; Coupled; Data; Databases; Development; Dimensions; Disadvantaged; Disease model; Distal; Dose; Education and Outreach; Educational Models; Environment; Environment Design; Epigenetic Process; Evaluation; Genetic Transcription; Immune; Immune system; Immunologic Factors; Immunooncology; Immunotherapy; Individual; Internships; K-12 Education; Kinetics; Knowledge; Lead; Leadership; Malignant Neoplasms; Measurement; Metastatic Melanoma; Metastatic malignant neoplasm to brain; Mitogen-Activated Protein Kinase Inhibitor; Modeling; Nature; Neoplasm Metastasis; Outcome; Patients; Pharmaceutical Preparations; Pilot Projects; Play; Primary Neoplasm; Proteomics; Research; Research Personnel; Research Project Grants; Resistance; Resources; Role; Science; Science, Technology, Engineering and Mathematics Education; Scientist; Self-Examination; Series; Structure; System; Systems Biology; Therapeutic; Time; Tissues; Visual; Work; biobank; cancer immunotherapy; combinatorial; computing resources; demographics; design; immune checkpoint blockade; in silico; in vivo; inhibitor; inhibitor therapy; medical schools; melanoma; model design; mouse model; multiple omics; outreach; outreach program; predicting response; prevent; programs; recruit; spatiotemporal; tool; transcriptomics; tumor; tumor-immune system interactions

Overall Project Summary The proposed U54 program Spatiotemporal Tumor Analytics for Guiding Sequential Targeted-Inhibitor -- Immunotherapy Combinations (ST-Analytics) is designed to develop the recent conceptual advance that targeted inhibitor + cancer immunotherapy (IT) combination treatments may yield significantly greater patient benefit if those treatments are administered in sequence rather than simultaneously. Analysis of retrospective clinical data coupled with in vivo therapeutic modeling using syngeneic models of murine melanoma strongly support this concept. In fact, the picture that has emerged in melanoma is that immune factors can play a strong role in driving resistance to MAPK inhibitor (MAPKi) therapy, and that lead-in immune checkpoint blockade (ICB) can ‘prime’ both the primary tumor and distal metastases (including brain metastases) for eradication when the IT is subsequently combined with MAPKi. This observation opens the doors for immune based strategies, such as ICB or adoptive cell therapy (ACT), as sequential combinatorial agents to prevent MAPKi resistance. However, this concept introduces a number of new variables, including dosing, sequence, and timing. This can make the design and execution of clinical trials that can yield statistically significant outcomes impractical. This is the scientific and translational problem we address in the proposed ST-Analytics U54. The ST-Analytics U54 center is populated by leading scientists at the ISB, the UCLA Geffen School of Medicine, and Yale, and is comprised of two research projects and two research cores, with each project integrating both state-of-the-art experimentation and computational work. This structure is further designed to bring together the scientific, experimental, and computational and administrative resources to develop a data base that captures the kinetics of lead-in monotherapy tumor priming, and apply that data base to the development of predictive in silico models that can inform the design of such targeted inhibitor – immunotherapy sequence combinations for clinical trials. This requires close integration and cycles of iteration between of state-of-the-art experimentation, leading edge computation, and realistic disease models, continuously calibrated through the analysis of highly relevant, biopsied patient tumors. The resulting science also provides exciting opportunities for high impact STEM outreach. We propose to act on those opportunities by leveraging a long-standing systems education outreach program at ISB that already has impacted K-12 STEM education in all 50 states, and places an emphasis on those communities that have been historically under-represented in STEM.

总体项目摘要 建议的U 54程序时空肿瘤分析指导顺序靶向抑制剂-- 免疫疗法组合（ST分析）旨在开发最近的概念性进展， 靶向抑制剂+癌症免疫疗法（IT）联合治疗可能会产生显著更大的患者 如果这些治疗是顺序而不是同时进行的话，回顾性分析 临床数据与使用鼠黑色素瘤的同基因模型的体内治疗建模相结合， 支持这个概念。事实上，在黑色素瘤中出现的情况是，免疫因素可以发挥强大的作用， 在驱动对MAPK抑制剂（MAPKi）治疗的抗性中的作用，以及导致免疫检查点阻断（ICB） 可以“准备"原发肿瘤和远端转移瘤（包括脑转移瘤）进行根除， IT随后与MAPKi结合。这一观察结果为基于免疫的策略打开了大门， 作为ICB或过继细胞疗法（ACT），作为预防MAPKi抗性的顺序组合剂。 然而，这一概念引入了许多新的变量，包括给药、顺序和时间。这可以 使临床试验的设计和执行，可以产生统计上显着的结果不切实际。这 是我们在拟议的ST-Analytics U 54中解决的科学和翻译问题。 ST-Analytics U 54中心聚集了ISB、加州大学洛杉矶分校格芬医学院的顶尖科学家， 和耶鲁大学，由两个研究项目和两个研究核心组成，每个项目都将两者结合起来。 最先进的实验和计算工作。这一结构的进一步设计是为了将 科学、实验、计算和管理资源，以开发一个数据库， 导入单药治疗肿瘤引发的动力学，并将该数据库应用于开发预测性肿瘤引发的方法。 计算机模型，其可以为设计这样的靶向抑制剂-免疫治疗序列组合提供信息， 临床试验这需要紧密的集成和最先进的实验之间的迭代周期， 前沿计算和现实的疾病模型，通过分析高度 相关的活检患者肿瘤。由此产生的科学也为高影响力提供了令人兴奋的机会 STEM外联。我们建议通过利用长期存在的系统教育， ISB的外展计划已经影响了所有50个州的K-12 STEM教育， 强调那些在STEM中历史上代表性不足的社区。

项目成果

The essence of phase transitions in condensed matter by an information theoretic approach.

通过信息论方法研究凝聚态相变的本质。

• DOI: 10.1073/pnas.2310281120
• 发表时间: 2023
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1
• 作者: Raz,T;Levine,RD
• 通讯作者: Levine,RD